





import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a__      :     str         =		logging.get_logger(__name__)

a__      :     Any         =		{
    "vocab_file": "vocab.txt",
    "merges_file": "bpe.codes",
}

a__      :     Optional[int]         =		{
    "vocab_file": {
        "vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt",
        "vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt",
    },
    "merges_file": {
        "vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes",
        "vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes",
    },
}

a__      :     Dict         =		{
    "vinai/phobert-base": 256,
    "vinai/phobert-large": 256,
}

def      UpperCAmelCase_    (    _UpperCAmelCase							:Tuple				)	->				Dict:
     '''simple docstring'''


     A_          =       set()
     A_          =       word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)				)
          A_          =       char

     A_          =       set(UpperCamelCase_				)
     return pairs





class UpperCAmelCase_				( snake_case__ ):
  __lowerCAmelCase   :						Any							=   VOCAB_FILES_NAMES
  __lowerCAmelCase   :						List[str]							=   PRETRAINED_VOCAB_FILES_MAP
  __lowerCAmelCase   :						Optional[Any]							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


  def __init__(							self ,__snake_case ,__snake_case ,__snake_case="<s>" ,__snake_case="</s>" ,__snake_case="</s>" ,__snake_case="<s>" ,__snake_case="<unk>" ,__snake_case="<pad>" ,__snake_case="<mask>" ,**__snake_case ,):



       """simple docstring"""





       super().__init__(
           bos_token=__snake_case ,eos_token=__snake_case ,unk_token=__snake_case ,sep_token=__snake_case ,cls_token=__snake_case ,pad_token=__snake_case ,mask_token=__snake_case ,**__snake_case ,)

       A_          =       vocab_file
       A_          =       merges_file

       A_          =       {}
       A_          =       0
       A_          =       1
       A_          =       2
       A_          =       3

       self.add_from_file(__snake_case )

       A_          =       {v: k for k, v in self.encoder.items()}

       with open(__snake_case ,encoding='''utf-8''' ) as merges_handle:
            A_          =       merges_handle.read().split('''\n''' )[:-1]
       A_          =       [tuple(merge.split()[:-1] ) for merge in merges]
       A_          =       dict(zip(__snake_case ,range(len(__snake_case ) ) ) )
       A_          =       {}


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case = None ):



       """simple docstring"""





       if token_ids_a is None:
            return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       A_          =       [self.cls_token_id]
       A_          =       [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case = None ,__snake_case = False ):



       """simple docstring"""





       if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=__snake_case ,token_ids_a=__snake_case ,already_has_special_tokens=__snake_case )

       if token_ids_a is None:
            return [1] + ([0] * len(__snake_case )) + [1]
       return [1] + ([0] * len(__snake_case )) + [1, 1] + ([0] * len(__snake_case )) + [1]


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case = None ):



       """simple docstring"""





       A_          =       [self.sep_token_id]
       A_          =       [self.cls_token_id]

       if token_ids_a is None:
            return len(cls + token_ids_a + sep ) * [0]
       return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]


  @property
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       return len(self.encoder )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       return dict(self.encoder ,**self.added_tokens_encoder )


  def 						__UpperCAmelCase     (							self ,__snake_case ):



       """simple docstring"""





       if token in self.cache:
            return self.cache[token]
       A_          =       tuple(__snake_case )
       A_          =       tuple(list(word[:-1] ) + [word[-1] + '''</w>'''] )
       A_          =       get_pairs(__snake_case )

       if not pairs:
            return token

       while True:
            A_          =       min(__snake_case ,key=lambda __snake_case : self.bpe_ranks.get(__snake_case ,float('''inf''' ) ) )
            if bigram not in self.bpe_ranks:
                 break
            A_   ,    A_          =       bigram
            A_          =       []
            A_          =       0
            while i < len(__snake_case ):
                 try:
                      A_          =       word.index(__snake_case ,__snake_case )
                 except ValueError:
                      new_word.extend(word[i:] )
                      break
                 else:
                      new_word.extend(word[i:j] )
                      A_          =       j

                 if word[i] == first and i < len(__snake_case ) - 1 and word[i + 1] == second:
                      new_word.append(first + second )
                      i += 2
                 else:
                      new_word.append(word[i] )
                      i += 1
            A_          =       tuple(__snake_case )
            A_          =       new_word
            if len(__snake_case ) == 1:
                 break
            else:
                 A_          =       get_pairs(__snake_case )
       A_          =       '''@@ '''.join(__snake_case )
       A_          =       word[:-4]
       A_          =       word
       return word


  def 						__UpperCAmelCase     (							self ,__snake_case ):



       """simple docstring"""





       A_          =       []

       A_          =       re.findall(R'''\S+\n?''' ,__snake_case )

       for token in words:
            split_tokens.extend(list(self.bpe(__snake_case ).split(''' ''' ) ) )
       return split_tokens


  def 						__UpperCAmelCase     (							self ,__snake_case ):



       """simple docstring"""





       return self.encoder.get(__snake_case ,self.encoder.get(self.unk_token ) )


  def 						__UpperCAmelCase     (							self ,__snake_case ):



       """simple docstring"""





       return self.decoder.get(__snake_case ,self.unk_token )


  def 						__UpperCAmelCase     (							self ,__snake_case ):



       """simple docstring"""





       A_          =       ''' '''.join(__snake_case ).replace('''@@ ''' ,'''''' ).strip()
       return out_string


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case = None ):



       """simple docstring"""





       if not os.path.isdir(__snake_case ):
            logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
            return
       A_          =       os.path.join(
           __snake_case ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
       A_          =       os.path.join(
           __snake_case ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )

       if os.path.abspath(self.vocab_file ) != os.path.abspath(__snake_case ):
            copyfile(self.vocab_file ,__snake_case )

       if os.path.abspath(self.merges_file ) != os.path.abspath(__snake_case ):
            copyfile(self.merges_file ,__snake_case )

       return out_vocab_file, out_merge_file




  def 						__UpperCAmelCase     (							self ,__snake_case ):



       """simple docstring"""





       if isinstance(__snake_case ,__snake_case ):
            try:
                 with open(__snake_case ,'''r''' ,encoding='''utf-8''' ) as fd:
                      self.add_from_file(__snake_case )
            except FileNotFoundError as fnfe:
                 raise fnfe
            except UnicodeError:
                 raise Exception(f'Incorrect encoding detected in {f}, please rebuild the dataset' )
            return

       A_          =       f.readlines()
       for lineTmp in lines:
            A_          =       lineTmp.strip()
            A_          =       line.rfind(''' ''' )
            if idx == -1:
                 raise ValueError('''Incorrect dictionary format, expected \'<token> <cnt>\'''' )
            A_          =       line[:idx]
            A_          =       len(self.encoder )







import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							StableDiffusionPipeline.from_pretrained(UpperCamelCase_				,torch_dtype=torch.floataa							)

     # load LoRA weight from .safetensors
     snake_case											=							load_file(UpperCamelCase_							)

     snake_case											=							[]

     # directly update weight in diffusers model
     for key in state_dict:
          # it is suggested to print out the key, it usually will be something like below
          # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

          # as we have set the alpha beforehand, so just skip
          if ".alpha" in key or key in visited:
               continue

          if "text" in key:
               snake_case											=							key.split('''.'''							)[0].split(LORA_PREFIX_TEXT_ENCODER + '''_'''							)[-1].split('''_'''							)
               snake_case											=							pipeline.text_encoder
          else:
               snake_case											=							key.split('''.'''							)[0].split(LORA_PREFIX_UNET + '''_'''							)[-1].split('''_'''							)
               snake_case											=							pipeline.unet

          # find the target layer
          snake_case											=							layer_infos.pop(0							)
          while len(UpperCamelCase_							) > -1:
               try:
                    snake_case											=							curr_layer.__getattr__(UpperCamelCase_							)
                    if len(UpperCamelCase_							) > 0:
                         snake_case											=							layer_infos.pop(0							)
                    elif len(UpperCamelCase_							) == 0:
                         break
               except Exception:
                    if len(UpperCamelCase_							) > 0:
                         temp_name += "_" + layer_infos.pop(0							)
                    else:
                         snake_case											=							layer_infos.pop(0							)

          snake_case											=							[]
          if "lora_down" in key:
               pair_keys.append(key.replace('''lora_down'''				,'''lora_up'''							)							)
               pair_keys.append(UpperCamelCase_							)
          else:
               pair_keys.append(UpperCamelCase_							)
               pair_keys.append(key.replace('''lora_up'''				,'''lora_down'''							)							)

          # update weight
          if len(state_dict[pair_keys[0]].shape							) == 4:
               snake_case											=							state_dict[pair_keys[0]].squeeze(3							).squeeze(2							).to(torch.floataa							)
               snake_case											=							state_dict[pair_keys[1]].squeeze(3							).squeeze(2							).to(torch.floataa							)
               curr_layer.weight.data += alpha * torch.mm(UpperCamelCase_				,UpperCamelCase_							).unsqueeze(2							).unsqueeze(3							)
          else:
               snake_case											=							state_dict[pair_keys[0]].to(torch.floataa							)
               snake_case											=							state_dict[pair_keys[1]].to(torch.floataa							)
               curr_layer.weight.data += alpha * torch.mm(UpperCamelCase_				,UpperCamelCase_							)

          # update visited list
          for item in pair_keys:
               visited.append(UpperCamelCase_							)

     return pipeline


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE     :					Dict      =       argparse.ArgumentParser()

 parser.add_argument(
     "--base_model_path", default=None, type=str, required=True, help="Path to the base model in diffusers format."
 )
 parser.add_argument(
     "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
 )
 parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
 parser.add_argument(
     "--lora_prefix_unet", default="lora_unet", type=str, help="The prefix of UNet weight in safetensors"
 )
 parser.add_argument(
     "--lora_prefix_text_encoder",
     default="lora_te",
     type=str,
     help="The prefix of text encoder weight in safetensors",
 )
 parser.add_argument("--alpha", default=0.75, type=float, help="The merging ratio in W = W0 + alpha * deltaW")
 parser.add_argument(
     "--to_safetensors", action="store_true", help="Whether to store pipeline in safetensors format or not."
 )
 parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")

 _SCREAMING_SNAKE_CASE     :					List[Any]      =       parser.parse_args()

 _SCREAMING_SNAKE_CASE     :					Optional[int]      =       args.base_model_path
 _SCREAMING_SNAKE_CASE     :					Dict      =       args.checkpoint_path
 _SCREAMING_SNAKE_CASE     :					List[str]      =       args.dump_path
 _SCREAMING_SNAKE_CASE     :					Dict      =       args.lora_prefix_unet
 _SCREAMING_SNAKE_CASE     :					List[str]      =       args.lora_prefix_text_encoder
 _SCREAMING_SNAKE_CASE     :					Optional[Any]      =       args.alpha

 _SCREAMING_SNAKE_CASE     :					Optional[int]      =       convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

 _SCREAMING_SNAKE_CASE     :					Tuple      =       pipe.to(args.device)
 pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)




